Prediction of air-to-blood partition coefficients of volatile organic compounds using genetic algorithm and artificial neural network
An artificial neural network (ANN) was constructed and trained for the prediction of air-to-blood partition coefficients of volatile organic compounds. The inputs of this neural network are theoretically derived descriptors that were chosen by genetic algorithm (GA) and multiple linear regression (M...
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| Veröffentlicht in: | Analytica chimica acta 2008-07, Vol.619 (2), p.157-164 |
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| creator | Konoz, Elahe Golmohammadi, Hassan |
| description | An artificial neural network (ANN) was constructed and trained for the prediction of air-to-blood partition coefficients of volatile organic compounds. The inputs of this neural network are theoretically derived descriptors that were chosen by genetic algorithm (GA) and multiple linear regression (MLR) features selection techniques. These descriptors are:
R maximal autocorrelation of lag 1 weighted by atomic Sanderson electronegativities (R1E+), electron density on the most negative atom in molecule (EDNA), maximum partial charge for C atom (MXPCC), surface weighted charge partial surface area (WNSA1), fractional charge partial surface area (FNSA2) and atomic charge weighted partial positive surface area (PPSA3). The standard errors of training, test and validation sets for the ANN model are 0.095, 0.148 and 0.120, respectively. Result obtained showed that nonlinear model can simulate the relationship between structural descriptors and the partition coefficients of the molecules in data set accurately. |
| doi_str_mv | 10.1016/j.aca.2008.04.065 |
| format | Article |
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R maximal autocorrelation of lag 1 weighted by atomic Sanderson electronegativities (R1E+), electron density on the most negative atom in molecule (EDNA), maximum partial charge for C atom (MXPCC), surface weighted charge partial surface area (WNSA1), fractional charge partial surface area (FNSA2) and atomic charge weighted partial positive surface area (PPSA3). The standard errors of training, test and validation sets for the ANN model are 0.095, 0.148 and 0.120, respectively. Result obtained showed that nonlinear model can simulate the relationship between structural descriptors and the partition coefficients of the molecules in data set accurately.</description><identifier>ISSN: 0003-2670</identifier><identifier>EISSN: 1873-4324</identifier><identifier>DOI: 10.1016/j.aca.2008.04.065</identifier><identifier>PMID: 18558108</identifier><identifier>CODEN: ACACAM</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Air - analysis ; Air-to-blood partition coefficient ; Algorithms ; Analytical chemistry ; Artificial neural network ; Chemistry ; Exact sciences and technology ; General, instrumentation ; Genetic algorithm ; Models, Genetic ; Multiple linear regression ; Neural Networks (Computer) ; Organic Chemicals - blood ; Volatilization</subject><ispartof>Analytica chimica acta, 2008-07, Vol.619 (2), p.157-164</ispartof><rights>2008 Elsevier B.V.</rights><rights>2008 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c478t-7937b4774e8826cbff5cfa48d14ec65a6999b80ae8af3ed84f29addbbc30f3663</citedby><cites>FETCH-LOGICAL-c478t-7937b4774e8826cbff5cfa48d14ec65a6999b80ae8af3ed84f29addbbc30f3663</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.aca.2008.04.065$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3548,27923,27924,45994</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=20439742$$DView record in Pascal Francis$$Hfree_for_read</backlink><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/18558108$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Konoz, Elahe</creatorcontrib><creatorcontrib>Golmohammadi, Hassan</creatorcontrib><title>Prediction of air-to-blood partition coefficients of volatile organic compounds using genetic algorithm and artificial neural network</title><title>Analytica chimica acta</title><addtitle>Anal Chim Acta</addtitle><description>An artificial neural network (ANN) was constructed and trained for the prediction of air-to-blood partition coefficients of volatile organic compounds. The inputs of this neural network are theoretically derived descriptors that were chosen by genetic algorithm (GA) and multiple linear regression (MLR) features selection techniques. These descriptors are:
R maximal autocorrelation of lag 1 weighted by atomic Sanderson electronegativities (R1E+), electron density on the most negative atom in molecule (EDNA), maximum partial charge for C atom (MXPCC), surface weighted charge partial surface area (WNSA1), fractional charge partial surface area (FNSA2) and atomic charge weighted partial positive surface area (PPSA3). The standard errors of training, test and validation sets for the ANN model are 0.095, 0.148 and 0.120, respectively. Result obtained showed that nonlinear model can simulate the relationship between structural descriptors and the partition coefficients of the molecules in data set accurately.</description><subject>Air - analysis</subject><subject>Air-to-blood partition coefficient</subject><subject>Algorithms</subject><subject>Analytical chemistry</subject><subject>Artificial neural network</subject><subject>Chemistry</subject><subject>Exact sciences and technology</subject><subject>General, instrumentation</subject><subject>Genetic algorithm</subject><subject>Models, Genetic</subject><subject>Multiple linear regression</subject><subject>Neural Networks (Computer)</subject><subject>Organic Chemicals - blood</subject><subject>Volatilization</subject><issn>0003-2670</issn><issn>1873-4324</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2008</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqFkc9u1DAQhy0EotvCA3BBucAtwf_iOOKEKqBIleAAZ8uxx4uXxF5sp4gH4L1xuiu4wWk0429-GvlD6BnBHcFEvDp02uiOYiw7zDss-gdoR-TAWs4of4h2GGPWUjHgC3SZ86G2lGD-GF0Q2feSYLlDvz4lsN4UH0MTXaN9aktspzlG2xx1Kv7-xURwzhsPoeQNu4uzLn6GJqa9Dt5UYDnGNdjcrNmHfbOHAKXO9byPyZevS6ODbba8LUbPTYA13ZfyI6ZvT9Ajp-cMT8_1Cn159_bz9U17-_H9h-s3t63hgyztMLJh4sPAQUoqzORcb5zm0hIORvRajOM4SaxBasfASu7oqK2dJsOwY0KwK_TylHtM8fsKuajFZwPzrAPENSsxUiL4yP4LMtYPgvItkZxAk2LOCZw6Jr_o9FMRrDZJ6qCqJLVJUpirKqnuPD-Hr9MC9u_G2UoFXpwBnY2eXdLB-PyHo5izceC0cq9PHNQ_u_OQVN4cmWo0gSnKRv-PM34DXdGylQ</recordid><startdate>20080707</startdate><enddate>20080707</enddate><creator>Konoz, Elahe</creator><creator>Golmohammadi, Hassan</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7U5</scope><scope>8FD</scope><scope>L7M</scope><scope>7X8</scope></search><sort><creationdate>20080707</creationdate><title>Prediction of air-to-blood partition coefficients of volatile organic compounds using genetic algorithm and artificial neural network</title><author>Konoz, Elahe ; Golmohammadi, Hassan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c478t-7937b4774e8826cbff5cfa48d14ec65a6999b80ae8af3ed84f29addbbc30f3663</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2008</creationdate><topic>Air - analysis</topic><topic>Air-to-blood partition coefficient</topic><topic>Algorithms</topic><topic>Analytical chemistry</topic><topic>Artificial neural network</topic><topic>Chemistry</topic><topic>Exact sciences and technology</topic><topic>General, instrumentation</topic><topic>Genetic algorithm</topic><topic>Models, Genetic</topic><topic>Multiple linear regression</topic><topic>Neural Networks (Computer)</topic><topic>Organic Chemicals - blood</topic><topic>Volatilization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Konoz, Elahe</creatorcontrib><creatorcontrib>Golmohammadi, Hassan</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>Technology Research Database</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>MEDLINE - Academic</collection><jtitle>Analytica chimica acta</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Konoz, Elahe</au><au>Golmohammadi, Hassan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of air-to-blood partition coefficients of volatile organic compounds using genetic algorithm and artificial neural network</atitle><jtitle>Analytica chimica acta</jtitle><addtitle>Anal Chim Acta</addtitle><date>2008-07-07</date><risdate>2008</risdate><volume>619</volume><issue>2</issue><spage>157</spage><epage>164</epage><pages>157-164</pages><issn>0003-2670</issn><eissn>1873-4324</eissn><coden>ACACAM</coden><abstract>An artificial neural network (ANN) was constructed and trained for the prediction of air-to-blood partition coefficients of volatile organic compounds. The inputs of this neural network are theoretically derived descriptors that were chosen by genetic algorithm (GA) and multiple linear regression (MLR) features selection techniques. These descriptors are:
R maximal autocorrelation of lag 1 weighted by atomic Sanderson electronegativities (R1E+), electron density on the most negative atom in molecule (EDNA), maximum partial charge for C atom (MXPCC), surface weighted charge partial surface area (WNSA1), fractional charge partial surface area (FNSA2) and atomic charge weighted partial positive surface area (PPSA3). The standard errors of training, test and validation sets for the ANN model are 0.095, 0.148 and 0.120, respectively. Result obtained showed that nonlinear model can simulate the relationship between structural descriptors and the partition coefficients of the molecules in data set accurately.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><pmid>18558108</pmid><doi>10.1016/j.aca.2008.04.065</doi><tpages>8</tpages></addata></record> |
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| source | MEDLINE; ScienceDirect Journals (5 years ago - present) |
| subjects | Air - analysis Air-to-blood partition coefficient Algorithms Analytical chemistry Artificial neural network Chemistry Exact sciences and technology General, instrumentation Genetic algorithm Models, Genetic Multiple linear regression Neural Networks (Computer) Organic Chemicals - blood Volatilization |
| title | Prediction of air-to-blood partition coefficients of volatile organic compounds using genetic algorithm and artificial neural network |
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